Method of analyzing and processing signals

ABSTRACT

A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyse the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analogue to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyser component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyser component.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 10/547,430,filed Dec. 1, 2005, which is the United States National Stage ofInternational Patent Application No. PCT/GB2004/000808, filed Feb. 27,2004, which designated the United States and which claims the benefitunder 35 U.S.C. §119 of Foreign Priority Applications GB0304413.8, filedFeb. 27, 2003, GB0305168.7, filed Mar. 7, 2003, and GB0403066.4, filedFeb. 12, 2004, each of which is hereby incorporated herein by referencein its respective entirety.

1. INTRODUCTION Problem Domain/Field of Invention

The present invention relates to a method of analysing and processingsignals. More specifically the invention relates to the analysis andprocessing of photoplethysmogram (PPG) signals. The invention useswavelet transform methods to derive clinically useful information fromthe PPG including information regarding the respiration, pulse, oxygensaturation, and patient movement. This information may be used within adevice, to monitor the patient within a range of environments includingthe hospital and home environments. In one preferred embodiment thedevice may be used to detect irregularities in one or more of thederived signals: respiration, pulse, oxygen saturation and movement. Thedevice allows output of this information in a clinically useful form andincorporates an alarm which is triggered when one or a combination ofsignal irregularities are detected. Of particular note is that theutility of current pulse oximeter devices is greatly increased throughthe provision of a robust measure of patient respiration directly fromthe PPG signal.

2. BACKGROUND

2.1 Blood Oxygen Saturation and its Measurement

Oximetry is an optical method for measuring oxygen saturation in blood.Oximetry is based on the ability of different forms of haemoglobin toabsorb light of different wavelengths. Oxygenated haemoglobin (HbO₂)absorbs light in the red spectrum and deoxygenated or reducedhaemoglobin (RHb) absorbs, light in the near-infrared spectrum. When redand infrared light is passed through a blood vessel the transmission ofeach wavelength is inversely proportional to the concentration of HbO₂and RHb in the blood. Pulse oximeters can differentiate the alternatinglight input from arterial pulsing from the constant level contributionof the veins and other non-pulsatile elements. Only the alternatinglight input is selected for analysis. Pulse oximetry has been shown tobe a highly accurate technique. Modern pulse oximeter devices aim tomeasure the actual oxygen saturation of the blood (SaO₂) byinterrogating the red and infrared PPG signals. This measurement isdenoted SpO₂. The aim of modern device manufacturers is to achieve thebest correlation between the pulse oximeter measurement given by thedevice and the actual blood oxygen saturation of the patient. It isknown to those skilled in the art that in current devices a ratioderived from the photoplethysmogram (PPG) signals acquired at thepatients booty is used to determine the oxygen saturation measurementusing a look up table containing a pluracy of corresponding ratio andsaturation values. Modern pulse oximeter devices also measure patientheart rate. Current devices do not provide a measure of respirationdirectly from the PPG signal. Additional expensive and obtrusiveequipment is necessary to obtain this measurement.

2.2 Time-Frequency Analysis in Wavelet Space

The wavelet transform of a signal x(t) is defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {\mathbb{d}t}}}}} & \lbrack 1\rbrack\end{matrix}$where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (1) can be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation or atime-frequency representation where the characteristic frequencyassociated with the wavelet is inversely proportional to the scale a. Inthe following discussion ‘time-scale’ and ‘time frequency’ may beinterchanged. The underlying mathematical detail required for theimplementation within a time-scale or time-frequency framework can befound in the general literature, e.g. the text by Addison (2002).

The energy density function of the wavelet transform, the scalogram, isdefined asS(a,b)=|T(a,b)|²  [2]where ‘| |’ is the modulus operator. The scalogram may be resealed foruseful purpose. One common repealing is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & \lbrack 3\rbrack\end{matrix}$and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. We also include as a definition of a ridgeherein paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the plane welabel a ‘maxima ridge’. For practical implementation requiring fastnumerical computation the wavelet transform may be expressed in Fourierspace and the Fast Fourier Transform (FFT) algorithm employed. However,for a real time application the temporal domain convolution expressed byequation (1) may be more appropriate. In the discussion of thetechnology which follows herein the ‘scalogram’ may be taken to theinclude all reasonable forms of repealing including but not limited tothe original unsealed wavelet representation, linear rescaling and anypower of the modulus of the wavelet trans forts may be used in thedefinition.

As described above the time-scale representation of equation (1) may beconverted to a time-frequency representation. To achieve this, we mustconvert from the wavelet a scale (which can be interpreted as arepresentative temporal period) to a characteristic frequency of thewavelet function. The characteristic frequency associated with a waveletof arbitrary a scale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & \lbrack 4\rbrack\end{matrix}$where f_(c), the characteristic frequency of the mother wavelet (i.e. ata=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in the method describedherein. One of the most commonly used complex wavelets, the Morletwavelet, is defined as;ψ(t)=π^(−1/4)(e ^(12πf) ⁰ ^(s) −e ^(−(2πf) ⁰ ⁾ ³ ^(/2))e ^(−t) ³^(/2)  [5]where f₀ is the central frequency of the mother wavelet. The second termin the brackets is known as the correction term, as it corrects for thenon-zero mean of the complex sinusoid within the Gaussian window. Inpractice it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi(t)} = {\frac{1}{\pi^{1/4}}{\mathbb{e}}^{{\mathbb{i}2\pi}\; f_{0}t}{\mathbb{e}}^{{- t^{3}}/2}}} & \lbrack 6\rbrack\end{matrix}$

This wavelet is simply a complex wave within a Gaussian envelope. Weinclude both definitions of the Morlet wavelet in our discussion here.However, note that the function of equation (6) is not strictly awavelet as it has a non-zero mean, i.e. the zero frequency term of itscorresponding energy spectrum is non-zero and hence it is inadmissible.However, it will be recognised by those skilled in the art that it canbe used in practice with f₀>>0 with minimal error and we include it andother similar near wavelet functions in our definition of a waveletherein. A more detailed overview of the underlying wavelet theory,including the definition of a wavelet function, can be found in thegeneral literature, e.g. the text by Addison (2002). Herein we show howwavelet transform features may be extracted from the waveletdecomposition of pulse oximeter signals and used to provide a range ofclinically useful information within a medical device.

3. WAVELET FEATURE EXTRACTION

In this section, methods are described for the extraction and use ofwavelet features from the PPG signals for use in the provision ofclinically useful information. These are incorporated within a medicaldevice and the information is output in a range of formats for use inthe monitoring of the patient. The device comprises four key componentsfor the utilisation of the wavelet transform information, these are thePulse Component, Respiration Monitoring Component, Oxygen SaturationComponent and the Movement Component. The underlying theory pertainingto these components is detailed below.

3.1 Pulse Component

Pertinent repeating features in the signal gives rise to atime-frequency band in wavelet space or a resealed wavelet space. Forexample the pulse component of a photoplethysmogram (PPG) signalproduces a dominant band in wavelet space at or around the pulsefrequency. FIGS. 1( a) and (b) contains two views of a scalogram derivedfrom a PPG signal. The figures show an example of the band caused by thepulse component in such a signal. The pulse band is located between thedashed lines in the plot of FIG. 1( a) The band is formed from a seriesof dominant coalescing features across the scalogram. This can beclearly seen as a raised band across the transform surface in FIG. 1( b)located within a region at just over 1 Hz, i.e. 60 breaths per minute.The maxima of this band with respect to frequency is the ridge. Thelocus of the ridge is shown as a black curve on top of the band in FIG.1( b). By employing a suitable rescaling of the scalogram, such as thatgiven in equation 3, we can relate the ridges found in wavelet space tothe instantaneous frequency of the signal. In this way the pulsefrequency (pulse rate) may be obtained, from the PPG signal. Instead ofrepealing the scalogram, a suitable predefined relationship between thefrequency obtained from the ridge on the wavelet surface and the actualpulse frequency may also be used to determine the pulse rate.

By mapping the time-frequency coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way both times betweenindividual pulses and the timing of components within each pulse can bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, etc. Alternative definitions of a ridge may beemployed. Alternative relationships between the ridge and the pulsefrequency may be employed.

3.2 Respiration Monitoring Component

The respiration monitoring component uses wavelet based methods for themonitoring of patient respiration. This can include the measurement ofbreathing rate and the identification of abnormal breathing patternsincluding the cessation of breathing. A key part of the respirationmonitoring component is the use of secondary wavelet feature decoupling(SWFD) described below. The information concerning respiration gainedfrom the application of SWFD can then be compared and/or combined withrespiration information from other methods to provide a respirationmeasure output.

As stated above, pertinent repeating features in the signal give rise toa time-frequency band in wavelet space or a rescaled wavelet space. Fora periodic signal this band remains at a constant frequency level in thetime frequency plane. For many real signals, especially biologicalsignals, the band may be non-stationary) varying in characteristicfrequency and/or amplitude over time. FIG. 2 shows a schematic of awavelet transform of a signal containing two pertinent componentsleading to two bands in the transform apace. These bands are labeledband A and band B on the three-dimensional (3-D) schematic of thewavelet surface. We define the band ridge as the locus of the peakvalues of these bands with respect to frequency. For the purposes of thediscussion of the method we assume that band B contains the signalinformation of interest. We will call this the ‘primary band’. Inaddition, we assume that the system from which the signal originates,and from which the transform is subsequently derived, exhibits some formof coupling between the signal components in band A and band B.

When noise or other erroneous features are present in the signal withsimilar spectral characteristics of the features of band B then theinformation within band B can become ambiguous, i.e. obscured,fragmented or missing. In this cane the ridge of band A can be followedin wavelet space and extracted either as an amplitude signal or afrequency signal which we call the ‘ridge amplitude perturbation (RAP)signal’ and the ‘ridge frequency perturbation (RFP) signal’respectively. The RAP and RFP signals are extracted by projecting theridge onto the time-amplitude or time-frequency planes respectively. Thetop plots of FIG. 3 shows a schematic of the RAP and RFP signalsassociated with ridge A in FIG. 2. Below these RAP and RFP signals wecan see schematics of a further wavelet decomposition of these newlyderived signals. This secondary wavelet decomposition allows forinformation in the spectral region of band B in FIG. 2 to be madeavailable as band C and band D. The ridges of bands C and D can serve asinstantaneous time-frequency characteristic measures of the signalcomponents causing bands C and D. This method, which we call SecondaryWavelet Feature Decoupling (SWFD), therefore allows informationconcerning the nature of the signal components associated with theunderlying physical process causing the primary band B (FIG. 2) to beextracted when band B itself is obscured in the presence of noise orother erroneous signal features.

An example of the SWFD method used on a PPG signal to detect patientbreathing from the ridge associated with patient pulse is shown in FIGS.4 and 5. Daring the experiment from which the signal was taken thepatient was breathing regularly at breaths of 6 seconds duration (=0.167Hz).

FIG. 4( a) contains the scalogram derived from the PPG trace takenduring the experiment. Two dominant bands appear in the plots the pulseband and a band associated with patient breathing. These are marked Pand B respectively in the plot. In this example we are concerned withthe detection of breathing through time and hence here the breathingband is the primary band. The pulse band appears at just over 1 Hz, or60 beats per minute, the beat frequency of the heart and the breathingband appears at 0.167 Hz corresponding to the respiration rate. However,the identification of breathing features is often masked by other lowfrequency artefact in these signals. One such low frequency artefactfeature, ‘F’, is indicated in the plot within the dotted ellipse markedon the scalogram where it can be seen to interfere with the breathingband. FIG. 4( b) contains a 3-D view of the scalogram plot shown in FIG.4( a). From the 3-D plot we can see that the low frequency artefactfeature causes a bifurcation of the breathing band at the location,shown by the arrow in the plot. The pulse ridge is also shown on FIG. 4(b), indicated by the black curve along the pulse band. This is the locusof the maxima with respect to frequency along the pulse band.

FIG. 4( c) contains the RAP signal derived from, the pulse ridge shownin FIG. 4( b) where the pulse ridge is followed and its amplitude isplotted against time. The top plot of FIG. 4( c) contains the whole RAPsignal. The lower plot of FIG. 4( c) contains a blow up of the RAPsignal over a 50 seconds interval. An obvious modulation with a periodof 6 seconds can be seen in this blow up. The top plot of FIG. 4( d)contains the whole RFP signal corresponding to the pulse ridge in FIG.4( b). The lower plot of FIG. 4( d) contains a blow up of the RFP signalover 50 seconds. Again an obvious modulation (of 6 second period) can beseen in this blow up.

A second wavelet transform was then performed on the RAP and RFPsignals. The resulting scalograms corresponding to the RAP and RFPsignals are shown in FIGS. 5 a and 5 b respectively and the 3-D plots ofthese scalograms are shown in FIGS. 5 c and 5 d respectively. Thebreathing ridges derived from the RAP and RFP scalograms aresuperimposed on the 3-D scalograms. The RAP scalogram is the cleaner ofthe two and can be seen not to contain interference from the artefactfeature ‘F’ found in the original signal scalogram of FIG. 4( a). Forthis example the RAP scalogram provides the best solution for theremoval of erroneous signal features and the identification of thebreathing band when compared to the original scalogram and the RFPscalogram. In practice all three scalograms are compared and the optimalscalogram or combination of scalograms for the extraction of theinformation required is determined.

Through experimentation covering a variety of patient groups (e.g.adult, child, neonate) we have found that for certain signals the methodcan be enhanced by incorporating paths displaced from the band ridge inthe SWFD method. In these cases the SAP signals derived from thedisplaced path exhibits much larger oscillations (compared to the lowfrequency background waveform) than those of the original ridge path. Wefind that this enhancement allows us to better detect the breathingcomponent within the SWFD method. Hence we extend our definition of asurface ridge as employed in the method to include paths displaced fromthe locus of the peak values, contours at a selected level of the pulseband, and in general any reasonably constructed path within the vicinityof the pertinent feature under investigation, where the vicinity istaken to be within the region, of the corresponding band.

From the above example it can be seen how a secondary wavelet transformof wavelet transform ridge information derived from the pulse band ridgemay be used to provide a clearer manifestation of the breathing featuresin wavelet space from which pertinent breathing information may bederived.

The SWFD method described above can form the basis of completely newalgorithms for incorporation within devices which require the detectionof otherwise masked signal components. Herein, we show the applicationof the method to the detection of breathing features from within thephotoplethysmogram, although it will be recognised by those skilled inthe art that the method may be applied to other problematic signals.

In practice, both the original direct observation of the primary bandand the indirect observation through perturbations to the secondary bandmay be employed simultaneously and the optimal time-frequencyinformation extracted.

Those skilled in the art will recognise that modifications andimprovements can be incorporated within the methodology outlined hereinwithout departing from the scope of the Invention.

Those skilled in the art will recognise that the above methods may beperformed using alternative time-frequency representations of thesignals where the amplitude in the time-frequency transform space can berelated to the amplitude of pertinent features within the signal. Inaddition the decomposition of the original signal and the subsequentdecompositions of the RFP and RAP scalograms may be performed, each witha different time-frequency method. However, in the preferred method thecontinuous wavelet transform is employed in all decompositions, althoughdifferent wavelet functions may be employed in each of the wavelettransforms employed in the method.

The preferred method detailed herein departs from alternate methods toprobe the time-frequency information, within wavelet space which followpaths of constant frequency in wavelet space. The current methodinvolves following a selected path in wavelet space from which newsignals are derived. This allows signal components with non-stationaryfrequency characteristics to be followed and analysed to provideinformation of other signal components which may also exhibitnon-stationary behaviour.

It will be obvious to those skilled in the art that the method relies onhigh resolution in wavelet space hence the continuous wavelet, transformis the preferred method. (The time-frequency discretisation employed bythe discrete wavelet transform and the stationary wavelet transform is,in general, too coarse for the useful application of the method.) Thecontinuous wavelet, transform is implemented in the method through afine discretisation in both time and frequency.

Although the method herein has been described in the context of thedetection of breathing features from the pulse band of the wavelettransform of the photoplethysmogram, those skilled in the art willrecognise that the method has wide applicability to other signalsincluding, but not limited to: other biosignals (e.g. theelectrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, andultrasound), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound, and speech signals, chemical signals, and meteorologicalsignals including climate signals.

In summary a method for the decomposition of signals using wavelettransforms has been described which allows for underlying signalfeatures which are otherwise masked to be detected. The method isdescribed in the following steps

-   -   (a) A wavelet transform decomposition of the signal is made.    -   (b) The transform surface is inspected in the vicinity of the        characteristic frequency of the pertinent signal feature to        detect the dominant band (the primary band) associated with the        pertinent feature. This band is then interrogated to reveal        information corresponding to the pertinent feature. This        interrogation may include ridge following methods for        identification of localised frequencies in the time-frequency        plane.    -   (c) A secondary band is then identified outwith the region of        the pertinent feature and its ridge identified.    -   (d) The time-frequency and time-amplitude locus of points on the        secondary ridge are then extracted. These new signals are        denoted the ‘ridge amplitude perturbation (RAP) signal’ and the        ‘ridge frequency perturbation (RFP) signal’ respectively.    -   (e) A wavelet transformation of the RAP and RFP signals is then        carried out to give the RAP and RFP scalograms respectively.    -   (f) These secondary scalograms are then interrogated to reveal        information in the region of the primary band of the original        scalogram. This interrogation may include ridge following        methods for identification of localised frequencies in the        time-frequency plane.    -   (g) The information gained from step (b) and step (f) are then        used to provide the optimal signal information pertaining to the        signal feature or features under investigation.

More than one secondary band may be present. These additional secondarybands may be interrogated in the same way, i.e. steps (c) to (g).

In the context of breathing detection from the photoplethysmogram the‘primary band’ referred to in the above is the breathing band and the‘secondary band’ is the pulse band. In the method one or more or acombination of PPG signals may be employed.

In an alternative methodology once the RAP and RFP signals have beenabstracted, in step (d) these are then interrogated over short segmentsusing an alternative time-frequency or frequency based method (e.g.using a standard FFT routine to find a dominant peak associated with theprimary band signal) or another method of signal repetition including,but not limited to, turning points of the signal. This may be employedto speed up the computation of the characteristic frequency of the RAPand RFP scalogram bands or to enhance the technique.

In step (d) above a combination of the RAP and RFP signals may also beused to generate a representative signal for secondary waveletdecomposition.

Patient respiration information from the secondary wavelet featuredecoupling incorporating the RAP and RFP signals is used directly tomonitor patient respiration. This can include the measurement ofbreathing rate and the identification of abnormal breathing patternsincluding the cessation of breathing. Either the RAP-based SWFD or theRFP-based SWFD information may be chosen for patient respirationmonitoring. Alternatively a combination of both may be employed wherethe respiration information derived from each method may be gradedquantitatively according to a confidence measure.

Further the respiration information gained from the RAP-based SWFD andthe RFP-based SWFD may be compared to and/or combined with respirationinformation gained from other methods to provide an optimal output forrespiration measures including respiration rate, breath timings,breathing anomalies, etc. These other methods may include that describedin International Patent Application No PCT/GB02/02843, “Wavelet-basedAnalysis of Pulse Oximetry signals” by Addison and Watson. The chosenrespiration measure for output will be extracted using a pollingmechanism based on a quantitative measure of the quality of therespiration information derived by each method.

FIGS. 6 to 10 illustrate the preferred embodiment of the respirationmonitoring methodology. The wavelet transform of the PPG signal (FIG. 6(a)) is computed. A plot of the resulting scalogram is shown in FIG. 6(b). The 10 second PPG signal used in this example was taken from apremature neonate. The same methodology also works for adult and childPPGs. The pulse ridge is shown plotted as a black path across thescalogram in FIG. 6( b) at around 2.5 Hz—typical for these youngpatients. The RAP and RFP signals are then derived from the pulse ridgeof the wavelet transform. The RAP and RFP signals are shownrespectively. In FIG. 6( c) and FIG. 6( d). Also shown in FIG. 6( c) isthe patient switch signal which shows inspiration and expiration of thepatient as a high/low amplitude square wave trace. The switch signal wasactivated by an observer monitoring the movement of the chest wall ofthe neonate during the experiment. The turning points in the RAP and RFPsignals may be used as an initial detection mechanism for individualbreaths. The RFP and RAP signals are assessed for quality using aconfidence measure. This measure may be based on any reasonable measureincluding but not limited to the entropy of the signals. The signal withthe highest confidence is used to extract information on individualbreaths and a breathing rate using the average duration of a number ofrecently detected breaths. A second wavelet transform is performed onboth signals. The result of a second wavelet, transform on the RAPsignal of FIG. 6( c) is shown in FIG. 7( a) and the ridges of thistransform surface are extracted as shown in FIG. 7( b). The result of asecond wavelet transform on the RFP signal of FIG. 6( d) is shown inFIG. 7( c) and the ridges of this transform surface are extracted asshown in FIG. 7( d).

The extracted ridges from the RFP and RAP signal transforms and theridges found in the original transform in the region of respiration,shown in FIGS. 8( a), (b) and (c) respectively, are then analysed todetermine a composite path which we call the ‘selected respiration path’SRP. The analysis may include, but is not limited to, the intensitiesand locations of the ridges. The SRP represents the most likelybreathing components. The SRP derived from the extracted ridges shown inFIGS. 8( a), (b) and (c) is shown in FIG. 8( d). The SRP will normallybe determined within an initial pre-determined “latch-on” time windowand reassessed within an updated time window. The ridge selectionprocedure used to derive the SRP is based upon a decision treeimplementing a weighted branching dependent upon, tout not limited to,the following local (i.e. relationship between ridge components within aparticular ridge set) and global (i.e. the inter-relationship betweenridge components across ridge sets) criteria: start and end position,length, average and peak strengths, various spatial (i.e. movement rangeover the time-frequency surface) statistical parameters includingvariance and entropy, and a measurement of relative switchback positions(i.e. degree of overlap with other ridges). These criteria are based onresults of our in house experimentation across a range of patientcategories: adult, child and neonate.

A confidence metric for the accuracy of the SWFD ridge obtained from theRAP signal can also be acquired by comparing the resultant SWFD ridgeintensities derived from the RAP signal of the band maxima ridge andridges off-set from it. When compared to RAP-SWFD derived from the bandmaxima ridge, the off-ridge transform's ridges associated withrespiration have been observed to increase (to a maximum) in intensityas the displacement of the off-ridge from the maxima ridge is increased.Those ridges associated with other features, however, remain relativelystatic in amplitudes. In this way, by interrogating the ridge amplitudesof a plurality of RAP signals derived from the band maxima offsets, theridge or ridges associated with respiration can be identified through asignificant change in amplitude relative to others.

The selected ridge path (SRP) is then used to provide an overallconfidence as to breathing rate and/or provide individual breathmonitoring and/or prediction. By superimposing the SRP shown in FIG. 8(d) onto the phase information, derived from the original transform thephase along the SRP can be determined as shown in FIG. 9. In this wayindividual breaths may be identified through the behaviour of the phasecycling. This phase information along the SRP path may used to derive abreathing signal either by displaying the phase information as shown inFIG. 9 or by taking the cosine, or similar function, of the phaseinformation to produce a sinusoidal waveform, or by some other method toprovide a waveform of choice for visual display of the breathing signal.In an alternative embodiment the phase information from one of thesecondary transforms or a combination of the phase information from alltransforms may be used in the method. In addition, the phase informationused may be processed to remove erroneous phase information for examplecaused by movement artifact.

Parts of the SPR may contain missing segments caused by, for example,signal artefact. In these regions the SRP may be inferred using the lastavailable path point and the next available path point as shownschematically in FIG. 10. In the preferred embodiment this is carriedout using a linear fit between the points. However, other methods mayalso be used without departing from the scope of the invention.

3.3 Oxygen Saturation Measurement

The amplitude of signal features scale with their wavelet transformrepresentation. Thus by dividing components of the wavelet transform ofthe red PPG signal toy those of the infrared PPG signal we obtain newwavelet-based representations which contain useful information on thesignal ratios for use in the determination of oxygen saturation. If acomplex wavelet function is used this information may be extracted usinga suitable path defined on the ratio of the moduli of the transforms orusing a Lissajous plot from the real or imaginary parts of thetransforms. If a wavelet function containing only a real part isemployed then this information should be extracted using a Lissajousplot derived from the transforms. Two complimentary methods for theextraction of the wavelet-based ratio information required for thedetermination of oxygen saturation are given below.

FIG. 11 shows the three dimensional plots of the real-parts of thewavelet transforms of the simultaneously collected red and infrared. PPGsignals. A complex Morlet wavelet was used in the transform. Thedominant nature of the pulse band and breathing band regions is evidentin the figure. These are marked ‘B’ and ‘C’ respectively in the figure.A secondary band containing pulse components can also be seen in thefigure (marked ‘A’). This band is associated with the double humpedmorphology of the PPG waveform. In the new wavelet-based Lissajousmethod a number of frequency levels are selected within a moving window.The moving window is shown schematically on the plot in FIG. 12, (Herewe use a 4.56 second window for the purpose of illustration althoughalternative window lengths may be used as required.) The oscillatorynature of the pulse band and breathing band regions is evident in theplot. The wavelet transform values along each of these frequency levelsfor the red and infrared signals are plotted against each other to givea Wavelet-Based Lissajous (WBL) plot. This results in a multitude of WBLplots, one for each frequency level selected. In the method, theselected frequency levels lie in the range of expected pulse frequencieswhich is, for the purposes of illustration, herein defined as between0.67 and 3.33 Hz. This range may be altered to reflect the application.The multitude of WBL plots may be displayed together to form a 3-DLissajous figure, as shown in FIG. 13( a).

Note that, in the example shown here, a complex wavelet function wasused and hence both real or both imaginary values of the transform canbe utilised in the method. Further, information from the real WBL plotsand imaginary WBL plots may be combined to provide an optimal solution.If a real-only wavelet function is used (i.e. a wavelet functioncontaining only a real part and no imaginary part) than only one set oftransforms (real) are available to use.

Each Lissajous plot making up the 3-D Lissajous figure is then probed tofind its spread both along its principle axis and that axis orthogonalto it. To do this, any reasonable measure of spread may be used. Here weemploy the standard deviation (SD). FIG. 13( b) shows an end on view ofthe 3-D Lissajous of FIG. 13( a). The region of the 3-D Lissajous FIGS.13( a) and 13(b) in the vicinity of the pulse frequency is marked by theletter ‘B’ in the figures and higher frequencies are marked by theletter ‘A’. FIG. 14 contains plots of the standard deviation of dataspread along the principle axis (top plot) and minor axis (middle plot),and the ratio of the standard deviations (lower plot) for each Lissajouscomponent making up the 3-D Lissajous plot in FIG. 13( a). In thepreferred embodiment the Lissajous component with the maximum spread isused in the determination of the oxygen saturation. The location of thiscomponent is marked by the arrow in the top plot of FIG. 14. Thiscomponent, with the maximum spread along the major principle axis, isplotted in FIG. 13( c): the representative slope of which is computedand used to determine the local oxygen saturation value using apredefined look-up table. This maximum spread is usually found at ornear the pulse frequency. A check is also made on the SD ratios: definedas the SD of spread along the major axis divided by the SD of spreadalong the minor axis. A low SD ratio implies good correlation betweenthe two signals. The SD ratio for the component with maximum spread isindicated by the arrow in the lower plot of FIG. 14. We can see for thiscase that a relatively low SD ratio occurs at this location. The SDratio check may be used to pick a more appropriate wavelet-basedLissajous plot and can form part of a noise identification and/orreduction algorithm. Alternate methods of picking an optimalwavelet-based Lissajous may also be employed as appropriate. Duringperiods of excessive noise, the Lissajous components can become spreadout in shape, and in some cases the direction of the major and minorprinciple axis can significantly change from that of the relativelynoise free portions of the signals. A check can therefore be made todetermine if this has occurred by retaining a recent history of theselected Lissajous components. This can further be used as a confidencecheck on the selected Lissajous figure used in the determination ofoxygen saturation.

Note that the ratio of the amplitudes of the independent wavelet signalsmaking up the selected Lissajous component may also be used to determinethe oxygen saturation. Note also that the inverse transform of thesewavelet signals may be used in the method to determine oxygensaturation. The method described can be used to extract the pertinentratio information from wavelet transforms computed using either complexor real-only wavelet functions.

FIG. 15 shows the oxygen saturation determined using the 3-D Lissajousmethod (solid black line) compared with the traditional signal amplitudemethod (dotted) and signal Lissajous method (dashed). All three methodsemployed a 4 second smoothing window. It can be seen that for theparticular example signal interrogated here (the signals taken from thefinger of a healthy male patient aged 42 sitting in an upright positionat rest) the wavelet method produces a more consistent value.

FIG. 16 contains three-dimensional views of the red and infraredscalograms corresponding to an example PPG signal. Here the modulus ofthe complex transform is used. The locations of the band associated withthe pulse component are indicated in the plots (denoted ‘B’ in thefigures). We define the collection of points corresponding to the pathof the maxima of the band projected onto the time frequency plane as P.A wavelet ratio surface (R_(WT)) can be constructed by dividing thewavelet transform of the logarithm of red signal by the wavelettransform of the logarithm of the infrared signal to get atime-frequency distribution of the wavelet ratio surface, i.e.

$\begin{matrix}{R_{WT} = \frac{{T\left( {a,b} \right)}_{R}}{{T\left( {a,b} \right)}_{IR}}} & \lbrack 7\rbrack\end{matrix}$where where the subscripts R and IR identify the red and infraredsignals respectively. The wavelet ratio surface derived from the twoscalograms in FIG. 16 is shown schematically in FIG. 17. Note that asdescribed previously in our definition of scalogram we include allreasonable forms of resealing including the original unsealed waveletrepresentation, linear resealing and any power of the modulus of thewavelet transform may be used in the definition. As the amplitude of thewavelet components scale with the amplitude of the signal componentsthen for regions of the surface not affected by erroneous signalcomponents the wavelet ratio surface will contain values which can beused to determine the oxygen saturation using a pre-defined look-uptable.

As can be seen in FIG. 17, the time frequency wavelet ratio surfacealong, and in close proximity to, the projection of the pulse ridge pathP onto the wavelet ratio surface are stable and hence may be used in therobust determination of the oxygen saturation. In the preferredembodiment the values obtained along the projection of P onto R_(WT) areused to determine oxygen saturation via a pre-defined look-up tablewhich correlates R_(WT) to oxygen saturation.

A 2-D or 3-D view of the R_(WT) plot may be computed and displayed inreal time to provide a visual indication of the quality of the ratio ofratios obtained by the method, and hence the quality of the measurementof oxygen saturation.

FIG. 18 contains a plot of the end view of the wavelet ratio surfaceshown in FIG. 17. From the figure we see that a relatively stable, flatregion is also found at or near the respiration frequency (R in thefigure). It has bean noted from experimentation that for some cases therespiration region of the wavelet ratio surface may lie at a differentlevel from the pulse band region. Hence, for these cases, using R_(WT)obtained in the breathing region would produce erroneous values ofoxygen saturation. By following a path in the region of the pulse bandour method automatically filters out erroneous breathing components inthe signal.

FIG. 19 contains a plot of the oxygen saturation determined by thewavelet ratio surface method as a function of time as compared with twostandard methods: the traditional signal amplitude method and thetraditional Lissajous method. The PPG signals were again taken from thefinger of a healthy male patient aged 42 sitting in an upright positionat rest. From visual inspection of the plot it can be seen that, forthis example, the wavelet-based method produces a more consistent valueof oxygen saturation compared to contemporary methods.

It will be recognized by those skilled in the art that, in analternative embodiment, the pulse band ridge path P can also beprojected onto the real or imaginary transform components. From thevalues of the transform components along this path over a selected timeinterval a Lissajous figure may be obtained and used in thedetermination of oxygen saturation. It will also be recognised by thoseskilled in the art that, in an alternative embodiment, alternative pathsmay be projected onto the wavelet ratio surface and used for thedetermination of oxygen saturation. For example in regions where thepulse band exhibits noise causing the path of the ridge maxima to movefar from the actual pulse frequency a method for detecting such noisyevents and holding the path to the most appropriate recent pulsefrequency may be used until the event has passed or until a presetperiod of time whereby an alarm is triggered.

The 3-D Lissajous and wavelet ratio surface methodologies for thedetermination of oxygen saturation, as described, above, can form thebasis of an algorithm for incorporation, within pulse oximeter devices.Furthermore the ability of the methodologies to restrict themselves tothe optimal wavelet transform values by picking the optimal Lissajous orfollowing the pulse band respectively, allows for erroneous signal,elements to be discarded automatically; so leading to a more robustalgorithm for the determination of oxygen saturation.

Note that in both new methods the inverse transform of the selectedwavelet values may also be use, as they too scale with the signalfeatures.

In the preferred embodiment, both the 3-D Lissajous and wavelet ratiosurface methods are employed simultaneously and the optimal measuredsaturation value determined. It is obvious from the above descriptionthat the initial inputted signals and wavelet transformation of thesesignals form common elements to tooth methods.

Those skilled in the art will recognise that modifications andimprovements can be incorporated to the methodology outlined hereinwithout departing from the scope of the invention.

Those skilled in the art will recognise that the above methods may beperformed using alternative time-frequency representations of thesignals where the amplitude in the time-frequency transform space can berelated to the amplitude of pertinent features within the signal.However, in the preferred method the continuous wavelet transform isemployed.

In summary a method for the decomposition of pulse oximetry signalsusing wavelet transforms has been described which allows for underlyingcharacteristics which are of clinical use to be measured and displayed.These wavelet decompositions can then be used to:

-   (a) provide, using information derived from the signal wavelet    transforms (i.e. from the original transform, the repealed wavelet    transforms, the ratio of derived wavelet transforms, the scalograms,    wavelet ridges, etc.) a method for measuring oxygen saturation.-   (b) construct, using information derived from the wavelet transform    (i.e. from the original transform, the rescaled wavelet transforms,    the ratio of derived wavelet transforms, the scalograms, wavelet    ridges, etc.), a plurality of wavelet-based Lissajous figures from    which the optimum Lissajous representation is chosen using preset    criteria and the slope of which is used to determine the oxygen    saturation of the signal using a look-up table.-   (c) construct, using information derived from the wavelet transform    (i.e. from the original transform, the resealed wavelet transforms,    the ratio of derived wavelet transforms, the scalograms, wavelet    ridges, etc.), a time-frequency equivalent of the ratio of ratios,    the wavelet ratio surface, from which to determine the oxygen    saturation of the signal toy following a selected path through the    time frequency plane. The preferred path through the time frequency    plane to be that corresponding to the pulse band.-   (d) provide an optimal oxygen saturation value from those derived    in (b) and (c).    3.4 The Monitoring of Patient Movement

Current devices are configured to remove detrimental movement artifactfrom the signal in order to clean it prior to determination of theclinical parameter of interest, e.g. the pulse rate or oxygensaturation. However, the method described herein as embodied within adevice monitors general patient movement, including large scale bodymovements, respiration and the beating heart. In this way the absence ofpatient movement and/or irregularity of movement can be detected and analarm triggered.

Patient movement results in PPG signal artifact. The manifestation ofthis artifact can be observed in the wavelet transform of the signal. Anexample of a movement artifact in the scalogram is shown in FIG. 20( a).The PBS signal from which the wavelet plot was derived was acquired froma premature baby a few weeks after birth. The location of the movementartifact is marked by the arrow in the plot. The breathing band ridgehas been superimposed on the wavelet plot (marked R in the figure). Thepulse band is marked P in the figure. Notice that the artifact causes adrop-out in the detected breathing ridge (i.e. a missing fragment), andalso cuts through the pulse band where it can cause similar drop outs tooccur in the detection of the pulse ridge. It has been the focus ofpulse oximeter device manufacturers to remove as much of the movementartifact component from the signal while leaving the informationnecessary to obtain accurate oxygen saturation and pulse ratemeasurements. In a preferred embodiment of the methods described hereinwe extract a movement component from the PPG signals for use in themonitoring of patient movement and, in particular, for the monitoring ofthe movement of infants.

A three-dimensional view of the scalogram of FIG. 20( a) is plotted inFIG. 20( b). Here we see the dominance of the movement artifact featurein wavelet space. By identifying such features we can monitor patientmovement. It is common for young babies to exhibit very variablerespiration patterns and to cease breathing for short periods of time,especially when making a movement of the body. Hence inspecting thederived movement signal when an irregular respiration signal occurs,including cessation of breathing, gives a further measure of patientstatus.

The modulus maxima of the wavelet surface is the loci of the maxima ofthe wavelet surface with respect to time. FIG. 21( a) plots the modulusmaxima lines associated with FIG. 20( a). FIG. 21( b) shows athree-dimensional view of the transform surface with the modulus maximalines superimposed. FIG. 22( a) shows an end view of the maxima lines(without the surface shown) corresponding to those shown in FIGS. 21( a)and 21(b). We can see from the end view that the modulus maxima linecorresponding to the movement artifact has a significantly differentmorphology to the other maxima lines: it covers a large frequency rangeand contains significantly more energy than the other maxima, especiallyat low frequencies. By setting amplitude threshold criteria at afrequency or range of frequencies we can differentiate the modulusmaxima of the artifact from other features. An example of this is shownschematically by the threshold level and frequency range depicted onFIG. 22( b), where maxima above the pre-defined amplitude thresholdwithin a frequency range given by f₍₁₎<f<f₍₂₎ are identified ascorresponding to movement artifact. In addition a check of localanomalies in the detected pulse and breathing ridges may also be made.For example modulus maxima which are at significantly higher amplitudesthan the pulse ridge mean value in their vicinity are deemed tocorrespond to movement artifact. This is depicted in FIG. 22( c). Inaddition, modulus maxima which are at a significantly higher amplitudethan the respiration ridge mean value in their vicinity are deemed tocorrespond to movement artifact. This is depicted in FIG. 22( d).

A region in the time frequency plane within the support of the waveletis then deemed to contain artifact. The support of the wavelet is takenas a predefined measure of temporal ‘width’ of the wavelet. For waveletswith theoretical infinite width, such as the Morlet wavelet, the widthis defined in terms of the standard deviation of temporal, spread: forexample we use three times the standard deviation of spread each sidefrom the wavelet centre. Thus a cone of influence of the artifact may bedefined in the transform plane.

Using the above method we can monitor patient movement by detectingmodulus maxima corresponding to movement artifact. This information canbe used to monitor patient movement and/or to provide a measure ofconfidence on the derived values of other measurements (e.g. oxygensaturation, pulse and respiration). These measurements may, for examplebe held at a previous value until the detected movement event haspassed.

Other artefact may exist in the signal which may originate from thedrive and control electronics including, but not limited to, automaticgain adjustments. The occurrence of this type of artifact will be knownand can be accounted for in the signal and hence differentiated frommovement artifact.

4. DEVICE CONFIGURATION AND USAGE

The device may be used to monitor one or more of the following signals:respiration, pulse, breathing and movement. Useful information regardingthese signals would be displayed on the device or output in a suitableformat for use.

In one embodiment the device would be used to continually monitor one ormore of these signals.

In another embodiment the device would be used to monitor one or more ofthese signals intermittently.

4.1 Device Configuration

Detailed block diagrams of the device are provided in FIGS. 23, 24, 25and 26.

The following is with reference to FIG. 23. In the present inventionsignals are acquired at the patient's body 10. These are sent fordigitization 11. The links between components of the system may be fixedphysical or wireless links, for example radiofrequency links. Inparticular, either or both of the links between 10 and 11 or 11 and 12,or the links between the analyser component and a visual display may awireless link enabled by a radiofrequency transmitter. The digitisedcardiac signals 11 are sent to 12 where in the preferred embodiment thenatural logarithm of the signals are computed. These are then sent to 13where the wavelet transforms of the signals are performed. Thecomponents of the wavelet transformed signals, including modulus, phase,real part, imaginary part are then sent to 14 where the pulse ridge isidentified. The information from 13 and 14 is then used in theextraction of patient pulse information 15, oxygen saturation 16,patient movement information 17 and respiration information 18. Theinformation regarding oxygen saturation, pulse, respiration and patientmovement is all sent to the Analyser component 19 where it is collectedand collated ready for outputting at 20. The oxygen saturation,respiration, pulse rate and movement information is output from thedevice 20 through a number of methods, which may include a printout, adisplay screen or other visual device, an audable tone, andelectronically via a fixed or remote link. The output information may besent to a location remote from the patient, for example sent viatelephone lines, satellite communication methods, or other methods.Further, real-time wavelet-based visualisations of the signal (includingthe original transform and/or the wavelet ratio surface with projectedpulse ridge path) may be displayed on the device 20. Thesevisualisations will highlight salient information concerning the qualityof the outputted measurements. Additional useful information regardingmovement artefact and breathing information may be apparent from such areal time display.

The workings of components 15, 16, 17 and 18 shown in FIG. 23 aredescribed below in more detail.

Pulse Component 15: With reference to FIG. 23, pulse informationincluding pulse rate and pulse irregularities are derived at 15 usingthe instantaneous frequency of the pulse band ridge determined at 14.The instantaneous frequency may correspond directly with theinstantaneous ridge frequency or require a mapping from theinstantaneous ridge frequency and the true respiration rate. Further themethod allows for a smoothing of this value over a fixed time interval.Further the method allows for erroneous values of the pulse rate derivedin this way to be excluded from the outputted values. This component 15may also be used to measure inter-beat intervals and pertinent pulsewave timings. The pulse information determined at 15 is then sent to theAnalyser Component 19.

The Oxygen Saturation Component 16: The following is with reference toFIGS. 23 and 24. The oxygen saturation component 16 shown in FIG. 23comprises the subcomponents 31, 32, 33, 34, 35, 36 and 37 as shown inFIG. 24. The wavelet transform information and pulse ridge informationfrom 14 is input into this module at the feature sorter 31 which sendsthe relevant information to the Lissajous computation unit (components32, 33 and 34) and the pulse ridge computational unit (components 35 and36). A predetermined number of wavelet-based Lissajous are computed overthe pulse region 32. An automated procedure is employed for thedetermination of the optimal Lissajous for use in the oxygen saturatio,calculation 33. In the preferred embodiment this would be achieved bycomparing the standard deviations of the data spread along of theprinciple axes of the Lissajous plot. The slope of the principle axis isthen used to determine the oxygen saturation using a suitable look-uptable which correlates the slope to oxygen saturation 34. The oxygensaturation determined at 34 is denoted ‘Oxygen Saturation Determination(1)’.

The information regarding the wavelet transforms of the PPG signals andthe path of the pulse ridge is collected at the feature sorter 31 usedto compute the wavelet ratio surface 35. The wavelet ratio correspondingto the pulse path is determined the projecting the pulse path onto thewavelet ratio surface. This ratio is then used to determine the oxygensaturation using a look-up table which correlates the wavelet ratio tooxygen saturation 36. The oxygen saturation determined at 36 is denoted‘Oxygen Saturation Determination (2)’. The two oxygen saturation values(1) and (2) are then used to determine the most appropriate value ofoxygen saturation 37. This value is then sent to the Analyser Component19.

Movement Component 17: The following is with reference to FIGS. 23 and26. The Movement component 17 of FIG. 23 comprises the subcomponents 51,52, 53, 54, 55 as shown in FIG. 26. The wavelet transform informationand pulse ridge information is sent from 14 to the modulus maximacomponent 51 where the modulus maxima of the wavelet surfaces arecomputed. The modulus maxima information is then sent to be analysed formovement artifact. The modulus maxima information is sent to thecomponents 52, 53 and 54. These are described as follows. The Thresholdcomponent 52 detects maxima above a preset threshold and within a presetfrequency range which are them defined as movement artifact. The PulseCheck component 53 checks the maxima corresponding to the pulse band tosee if anomalously large excursion from the local mean level hasoccurred. If so movement artifact is detected. The Respiration Checkcomponent 54 checks the maxima in the vicinity of the selectedrespiration path SRP obtained from 18 to determine if anomalously largeexcursion from the local mean level has occurred. If so movementartifact is detected. The information from components 52, 53 and 54 arethen collected and collated at the Movement Signal component 55 where amovement signal is generated. This is then sent to the AnalyserComponent 19.

Respiration Component 18: The following is with reference to FIGS. 23and 25. The respiration component 17 of FIG. 23 comprises thesubcomponents 61, 62, 63 and 64 as shown in FIG. 25. The wavelettransform and pulse ridge information from 14 are input into this moduleat component 61 which uses the information to derive the ridge amplitudeperturbation (RAP) signal and the ridge frequency perturbation (RFP)signals. The RAP and RFP signals are derived using the path defined bythe projection of the maxima of the pulse band or a locus of pointsdisplaced from this maxima path. A secondary wavelet transform isperformed on these signals 62 and then, passed to the respirationdetection component 63 where the respiration ridges are detected for thewavelet transforms of the RFP and RAP signals. These are used within analgorithm which decides the selected respiration path (SRP). Thisalgorithm may also incorporate respiration information usingcomplementary methods 64. Note that in the method the original transformobtained at 13 and the secondary transform 62 may be computed usingdifferent wavelet functions. The respiration information is then seat tothe Analyzer Component 13 and also to the Movement component 17.

The Analyser Component 19: With reference to FIG. 23, the AnalyserComponent collects the information from the pulse component 15, OxygenSaturation Component 16. Movement Component 17 and Respiration Component18. During periods of detected motion or other signal artifact theanalyser makes a decision to hold the most appropriate recent values ofthese signals until the artifact event passes or until predeterminedinterval has passed at which point an alarm signal sent to the deviceoutput 20. Further the analyzer checks the incoming signals foranomalous behaviour including, but not limited to: low and or high pulserates, pulse irregularities, low and high breathing rates, breathingirregularities, low and high oxygen saturation rates, movementirregularities including excessive movement and absence of movement.Detected anomalous behaviour or combination of behaviours will triggeran alarm signal sent to the device output 20.

4.2 Physical Attachment of Probes and Transmission of PPG Signals

Referring to FIG. 23, the acquisition of the signal 10 takes place at asuitable site on the patient's body. This signal is then sent tocomponent 11 where the signals are digitized then to component 12 wheretheir natural logarithm is computed prior to the wavelet analysis at 13.The patient signal may be taken using a standard probe configuration.For example a finger or toe probe, foot probe, forehead probe, ear probeand so on. Further the probe may function in either transmittance orreflectance mode.

In one preferred embodiment for use with neonates a foot/ankle mounteddevice such as a cuff is employed as depicted schematically in FIG. 27.The cuff is used to house the probe electronics, radio frequencytransmitter modules and battery. FIG. 27( a) shows the patients lowerleg 80 and foot with the preferred embodiment of the cuff 83 attached tothe foot. The patients heel 81 and toes 82 protrude from the cuff. FIG.27( b) shows two views, one from each side of the foot showing the cuffwith compartments for housing the electronic equipment required forsignal acquisition and transmission. The PPG signals may be takendirectly through the foot using Light Emitting Diodes (LEDs) 86 andphotodetector 88 located as shown or, in an alternative embodiment, theymay be taken at the toe using a short length of cable attaching thepulse oximeter probe to the electronics contained in the cuff. In afurther alternative embodiment reflectance mode photoplethysmography maybe employed. In a further alternative embodiment more suitable for adultmonitoring the electronic equipment is packaged within a soft housingwhich is wrapped and secured around the wrist as shown in FIG. 28. Theelectronic components for receiving processing and transmitting the PPGsare housed in a unit 90 secured by a band 91 to the patients wrist. ThePPG signals are acquired at a site local to the wrist band. For examplefrom a finger 93 via a lead 92 from the wrist unit 90, or at the site ofthe wrist band and housing using, for example, reflectance modephotoplethysmography. In yet another alternative embodiment, the signalfrom the pulse oximeter probe would be sent to the monitor device usinga physical lead instead of the wireless method described here.

Light transmitters other than LEDs may be used in the device withoutdeparting from the scope of the invention.

In an alternative embodiment, the digitised signal from 11 may inputdirectly to the wavelet transform component 13 without taking thenatural logarithm. In an alternative embodiment, more than twowavelengths or combination of more than two wavelengths of light may beemployed in the Oximetry method.

4.3 Use of the Device

4.3.1 General Use

The device may be need, for general patient monitoring in the hospital,home, ambulatory or other environment. For example in a preferredembodiment for a device for use within a hospital setting it may be usedto continually or intermittently monitor patient respiration togetherwith oxygen saturation and pulse rate.

4.3.2 Embodiment as an Apnea Monitor

In another preferred embodiment of the device it would be used as anapnea monitor. Apnea is the cessation of breathing usually occurringduring sleep. There is increasing awareness of this sleep disorder asthe cause of a number of serious medical conditions in adults andinfants. Separate areas of use are envisaged for the device as an apneamonitor. Examples of this use include, but are not limited to: (1) adultmonitoring, where it can be used as a home screening diagnostic tool forpotential apnea patients and (2) infant monitoring, where it can be usedas either an in hospital or home monitoring tool to alert the child'scarer to this potentially fatal respiration irregularity.

Apnea monitors monitor heart and respiratory signals to detect apneaepisodes—usually defined as cessation of breathing for >20 seconds.Apnea is associated with slowing of the pulse (bradycardia) or bluishdiscoloration of the skin due to lack of oxygenated haemoglobin(cyanosis). Long term effects of apnea in adults are quite serious andhave been reported to include: heavy snoring, weariness and obsessivedrive to fall asleep, reduced physical and mental fitness, strokes,nervousness, fall in concentration and headaches, psychic symptoms up todepressions, sexual dysfunctions, impotence, dizziness and nightlyperspiration. In babies apnea may lead to death if suitableresuscitation measures are not taken.

As it measures respiration and movement directly from the pulse oximetersignal (in addition to oxygen saturation and pulse), the device can befitted remote from the head; e.g. the foot or arm of the patient. Thishas the advantage over current devices which comprise of probes locatedon the patients head and face to measure breathing at the patients noseand/or mouth. As such they are uncomfortable for adult patients and arequite impractical for fitting to babies for the obvious reason ofcausing a potential choking hazard. The preferred embodiment of ourinvention allows the PPG signal collected at the patient to be sent viaa wireless link to a remotely located device.

In summary, embodied as an apnea monitor, the device provides a methodfor the acquisition analysis and interpretation of pulse oximetersignals to provide clinically useful information on patient pulse rate,oxygen saturation, respiration and movement. From a combination of someor all of this information clinical decisions can be made with regard tothe patient's health. The patient respiration information is used tomonitor the patient in order to compute a respiration rate and to detectbreathing abnormalities, for example: apnea events, cessation inbreathing, sharp intakes of breaths, coughing, excessively fastbreathing, excessively slow breathing, etc. Information derived from oneor more of the respiration, movement, oxygen saturation and pulsemeasurements may be used to trigger an alarm to call for medical help orto initiate an automated process for the administration of a therapeuticintervention. A method may be employed for the archiving of the derivedsignals during the analysis period of the patient which may be at alater date for analysis by the clinician.

The device may be used to monitor the patient both during sleep and whenawake.

The device may be used to detect the onset of sudden infant deathsyndrome SIDS by detecting and analysing abnormalities in themeasurement of one or more of the following; oxygen saturation,respiration, movement and pulse.

4.3.3 Alarm

As described above, it is envisaged that the gathered information isused to trigger an alarm at the bedside and/or at a remote nursingstation. This alarm would be graded according to a classification ofpatient information. For example a reduction in oxygen saturation belowa predefined threshold with associated loss or irregularity of patientmovement, irregularity of pulse rate and loss or irregularity of patientrespiration could trigger the highest level of alarm, whereas areduction of oxygen saturation be low a predefined threshold with anormal level of patient movement and/or a regular respiration pattern,could trigger a lower level of alarm.

5. BRIEF DESCRIPTION OF DRAWINGS

FIG. 1( a): A wavelet transform surface showing the pulse band (locatedbetween the dashed lines). (High to Low energy is graded from white toblack in the grey scale plot.)

FIG. 1( b): Three-dimensional view of the wavelet transform surface ofFIG. 1( a) showing the maxima of the pulse band with respect tofrequency (the ridge) superimposed as a black path across the bandmaxima (High to bow energy is graded from white to black in the greyscale plot.)

FIG. 2: 3-D Schematic of a wavelet transform surface containing twobands. The locus of the local maxima, on the bands (the ‘ridges’) areshown by dashed lines.

FIG. 3. Schematics of the RAP (top left) and RFP (top right) signalsderived from ridge A in FIG. 1 together with their corresponding wavelettransforms shown below each (in 2D).

FIG. 4( a): The SWFD method as applied to a pulse oximetersignal—Scalogram of Original Signal. (High to Low energy is graded fromwhite to black in the grey scale plot.)

FIG. 4( b): The SWFD method as applied to a pulse oximeter signal—3-Dview of scalogram in (a) with the path of the pulse band ridgesuperimposed. (High to Low energy is graded from white to black in thegrey scale plot.)

FIG. 4( c): The SWFD method as applied to a pulse oximeter signal—RAPsignal (Top: full signal. Lower; blow up of selected region)

FIG. 4( d): The SWFD method as applied to a pulse oximeter signal—RFPsignal (Top: full signal. Lower; blow up of selected region)

FIG. 5( a): The SWFD method as applied to a pulse oximeter signal—RAPscalogram. (High to Low energy is graded from white to black in the greyscale plot.)

FIG. 5( b): The SWFD method as applied to a pulse oximeter signal—RFPscalogram. (High to Low energy is graded from white to black in the greyscale plot.)

FIG. 5( c): The SWFD method as applied to a pulse oximeter signal—3-Dview of RAP scalogram with breathing band ridge shown. (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 5( d): The SWFD method as applied to a pulse oximeter signal—3-Dview of RFP scalogram with ridge shown, (High to Low energy is gradedfrom white to black in the grey scale plot.)

FIG. 6( a): PPG Signal

FIG. 6( b): Pulse band and ridge corresponding to signal (a). (High toLow energy is graded from white to black in the grey scale plot.)

FIG. 6( c): RAP signal derived from ridge in (b) with breathing switch(square waveform) superimposed.

FIG. 6( d): RFP signal derived from ridge in (b)

FIG. 7( a): Wavelet Transform of RAP signal. (High to Low energy isgraded from white to black in the grey scale plot.)

FIG. 7( b): Extracted ridges from wavelet transform in (a). (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 7( c): Wavelet Transform of RFP signal. (High to Low energy isgraded from white to black in the grey scale plot.)

FIG. 7( d): Extracted ridges from wavelet transform in (c). (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 8( a): Breathing ridges extracted from the original wavelettransform

FIG. 8( b): Breathing ridges extracted from the secondary wavelettransform of the RAP signal

FIG. 8( c): Breathing ridges extracted from the secondary wavelettransform of the RFP signal

FIG. 8( d): Selected respiration path (SRP),

FIG. 9: Transform Phase along the SRP

FIG. 10: Filling in missing segments of the SRP

FIG. 11: Wavelet Representations of the Red PPG (top) and Infrared PPG(bottom)

FIG. 12: Schematic of the Sliding Window used to Obtain the WaveletComponents for the 3-D Lissajous

FIG. 13( a): Wavelet-based 3-Lissajous: 3-D View.

FIG. 13( b): Wavelet-based 3-D Lissajous: End on View of (a).

FIG. 13( c): Wavelet-based 3-D Lissajous: End on View of SelectedComponent.

FIG. 14: Standard Deviation of Lissajous Components in FIG. 3. Top plot:SD of principle component; Middle plot: SD of minor component; Lowerplot: Ratio of SD components. All three plots plotted against frequencyin Hz.

FIG. 15: Computed Oxygen Saturation curves. Dotted line: SignalAmplitude Method; Dashed Line traditional Signal Lissajous Method; SolidLine: Wavelet-based 3-D Lissajous Method.

FIG. 16: The red and infrared wavelet modulus surfaces corresponding toa 45 second segment of PPG signals. (High to Low energy is graded fromwhite to black in the grey scale plot.)

FIG. 17: The wavelet ratio surface derived from the division of the redby the infrared wavelet representations shown in FIG. 16.

FIG. 18: An end view of the wavelet ratio surface shown in FIG. 17.

FIG. 19: Computed Oxygen Saturation curves. Dotted lines: OxygenSaturation from Traditional Signal Amplitude Method; Dashed Line: OxygenSaturation from Traditional Signal Lissajous Method; Solid Line: OxygenSaturation from Traditional Wavelet-Ratio Surface Method

FIG. 20( a): Wavelet transform plot of a PPG signal taken from a youngbaby showing a corresponding to patient movement. Low to high energy isdepicted from black to white in the greyscale plot.

FIG. 20( b): Three-dimensional view of (a). Low to high energy isdepicted from black to white in the greyscale plot.

FIG. 21( a): Transform plot of FIG. 20( a) with modulus maximasuperimposed. Low to high energy is depicted from black to white in thegrayscale plot.

FIG. 21( b): Three-dimensional view of FIG. 21( a). Low to high energyis depicted from black to white in the greyscale plot.

FIG. 22( a): End view of modulus maxima lines in FIG. 21( b).

FIG. 22( b): Amplitude threshold method of identifying modulus maximaassociated with movement artefact

FIG. 22( c): Pulse ridge-based method of identifying modulus maximaassociated with movement artefact

FIG. 22( d): Respiration ridge-based method of identifying modulusmaxima associated with movement artefact

FIG. 23: Block diagram of device configuration

FIG. 24: Block diagram of subcomponents of oxygen saturation component(16) shown in FIG. 23

FIG. 25: Block diagram of subcomponents of respiration component (18)shown in FIG. 23

FIG. 26: Block diagram of subcomponents of movement component (17) shownin FIG. 23

FIG. 27( a): Schematic of foot cuff mounting: soft housing surroundingfoot used to hold monitoring apparatus.

80 patient leg; 81 patient heel; 82 patient toes; 83 soft housingsurrounding foot

FIG. 27( b): View from both sides of the envisaged device: preferredembodiment for neonatal monitor. 84 connection cabling; 85 RF componentsattached to housing; 86 LEDs; 87 pulse oximeter components attached tohousing; 88 photodetector. (Note LEDs and photodetector may also belocated on toe using short cable length from cuff.)

FIG. 28: Schematic of wrist cuff mounting; 90 electronic componenthousing; 91 wrist band; 92 connector cable; 93 finger probe

6. GENERAL

The invention has been described and shown with specific reference tospecific embodiments. However it will be understood by those skilled inthe art that changes to the form and details of the disclosedembodiments may be made without departing from the spirit and scope ofthe invention. For example signal transforms other than the wavelettransform may be used. Other variations may include using a multiplexedarrangement which alternates measurements for pulse, oxygen saturation,respiration and movement artefact using variations of the acquisitionequipment and transmission electronics. These variations may include butare not limited to the use of more than two wavelengths of light andvariable power and/or variable duty cycle to the light transmitters.

7. REFERENCE

-   Addison P. S., ‘The Illustrated Wavelet Transform Handbook’,    Institute of Physics Publishing, 2002, Bristol, UK.

The invention claimed is:
 1. A method for determining an artifactpertaining to physiological movement, comprising: obtaining aphotoplethysmographic (PPG) biosignal from a subject at least in partusing a signal acquisition unit; decomposing, using a signal processor,the PPG biosignal at least in part using a wavelet transform analysis toobtain transform data, the transform data comprising a time axis, ascale axis, and an amplitude axis; identifying, using the signalprocessor, a plurality of amplitude maxima points in the transform datain the direction of the time axis; setting, using the signal processor,an amplitude threshold; determining, using the signal processor, theartifact pertaining to physiological movement based at least in part onthe plurality of amplitude maxima points and the amplitude threshold;determining, using the signal processor, a physiological parameter ofthe subject based at least in part on the PPG biosignal, thephysiological parameter comprising at least on of: respiration rate,pulse rate, and oxygen saturation; and determining, using the signalprocessor, a confidence measurement pertaining to the physiologicalparameter based at least in part on determining the artifact.
 2. Themethod of claim 1, wherein the signal acquisition unit comprises aphotodetector.
 3. The method of claim 1, wherein the transform datacomprises data for a set of scales.
 4. The method of claim 3, whereinthe set of scales includes one or more scales corresponding to a pulseridge and the amplitude threshold is based at least in part on a meanvalue of the pulse ridge.
 5. The method of claim 3, wherein the set ofscales includes one or more scales corresponding to a respiration ridgeand the amplitude threshold is based at least in part on a mean value ofthe respiration ridge.
 6. The method of claim 1, further comprising:determining, using the signal processor, a region containing theartifact in the time-scale plane of the transform data; and defining,using the signal processor, in the region, a cone of influence of theartifact based at least in part on the widths of the wavelet functionused in the wavelet transform analysis.
 7. The method of claim 6,wherein the widths of the wavelet function are based at least in part ona standard deviation of temporal spread.
 8. The method of claim 1,further comprising holding, using the signal processor, measurementspertaining to a physiological parameter at a previous value based atleast in part on the artifact.
 9. The method of claim 1, furthercomprising: identifying, using the signal processor, the plurality ofamplitude maxima points in the transform data in the direction of thetime axis for at least two scales; setting, using the signal processor,one or more amplitude thresholds for the at least two scales; anddetermining, using the signal processor, the artifact pertaining tophysiological movement based at least in part on the plurality ofamplitude maxima points for the at least two scales and the one or moreamplitude thresholds.
 10. A monitoring system for determining anartifact pertaining to physiological movements, the system comprising: asignal acquisition unit attachable to a subject to obtain aphotoplethysmographic (PPG) biosignal; and a signal processor configuredto: decompose the PPG biosignal at least in part using a wavelettransform analysis to obtain transform data, the transform datacomprising a time axis, a scale axis, and an amplitude axis; identify aplurality of amplitude maxima points in the transform data in thedirection of the time axis; set an amplitude threshold; determine theartifact pertaining to physiological movement based at least in part onthe plurality of amplitude maxima points and the amplitude threshold;determine a physiological parameter of the subject based at least inpart on the PPG biosignal, the physiological parameter comprising atleast on of: respiration rate, pulse rate, and oxygen saturation; anddetermine a confidence measurement pertaining to the physiologicalparameter based at least in part on determining the artifact.
 11. Thesystem of claim 10, wherein the signal acquisition unit comprises aphotodetector.
 12. The system of claim 10, wherein the transform datacomprises data for a set of scales.
 13. The system of claim 12, whereinthe set of scales includes one or more scales corresponding to a pulseridge and the amplitude threshold is based at least in part on a meanvalue of the pulse ridge.
 14. The system of claim 12, wherein the set ofscales includes one or more scales corresponding to a respiration ridgeand the amplitude threshold is based at least in part on a mean value ofthe respiration ridge.
 15. The system of claim 10, wherein the signalprocessor is further configured to: determine a region containing theartifact in the time-scale plane of the transform data; and define, inthe region, a cone of influence of the artifact based at least in parton the widths of the wavelet function used in the wavelet transformanalysis.
 16. The system of claim 15, wherein the widths of the waveletfunction are based at least in part on a standard deviation of temporalspread.
 17. The system of claim 10, wherein the signal processor isfurther configured to hold measurements pertaining to a physiologicalparameter at a previous value based at least in part on the artifact.18. The system of claim 10, wherein the signal processor is furtherconfigured to: identify the plurality of amplitude maxima points in thetransform data in the direction of the time axis for at least twoscales; set one or more amplitude thresholds for the at least twoscales; and determine the artifact pertaining to physiological movementbased at least in part on the plurality of amplitude maxima points forthe at least two scales and the one or more amplitude thresholds.